Antenna Season Report Notebook¶

Josh Dillon, Last Revised January 2022

This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.

In [1]:
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
In [2]:
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
In [3]:
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "124"
csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
In [4]:
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))

Antenna 124 Report

In [5]:
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
In [6]:
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 43 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_
Found 41 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
In [7]:
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'

Load relevant info from summary CSVs¶

In [8]:
this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
            antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
        else:
            antnum = int(row['Ant'])
        if antnum != int(antenna):
            continue
        
        if np.issubdtype(type(row['Node']), np.integer):
            row['Node'] = str(row['Node'])
        if type(row['Node']) == str and row['Node'].isnumeric():
            row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break
In [9]:
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])

Table 1: Per-Night RTP Summary Info For This Atenna¶

This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.

In [10]:
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))

Antenna 124, Node N09:

Out[10]:
JDs A Priori Status Auto Metrics Flags Dead Fraction in Ant Metrics (Jee) Dead Fraction in Ant Metrics (Jnn) Crossed Fraction in Ant Metrics Flag Fraction Before Redcal Flagged By Redcal chi^2 Fraction ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score Average Dead Ant Metric (Jee) Average Dead Ant Metric (Jnn) Average Crossed Ant Metric Median chi^2 Per Antenna (Jee) Median chi^2 Per Antenna (Jnn)
2459858 digital_ok 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% -0.589978 0.453590 -0.737321 0.218667 1.083167 0.460961 0.623461 0.883217 0.7498 0.7163 0.4044 1.712033 1.486092
2459857 digital_ok 100.00% 100.00% 100.00% 0.00% - - 1.165751 1.021066 0.173916 0.184638 -0.915226 1.078582 0.075536 5.493836 0.0295 0.0279 0.0020 nan nan
2459856 digital_ok 0.00% 0.00% 0.00% 0.00% 3.43% 0.00% -0.878193 1.373387 0.182193 -1.049791 0.071181 -1.108517 0.004725 0.620455 0.7453 0.7267 0.3880 1.887864 1.535289
2459855 digital_ok 0.00% 0.00% 0.00% 0.00% 4.58% 8.40% -1.211728 1.608484 0.145927 -1.315217 -0.330233 -0.800515 -0.386425 -0.351288 0.7334 0.7429 0.4149 1.808280 1.461813
2459854 digital_ok 100.00% 0.00% 0.00% 0.00% 100.00% 0.00% -1.031321 1.791340 4.728353 6.873543 -0.165600 -0.380617 1.503638 1.610202 0.7504 0.7335 0.4335 4.430657 2.860593
2459853 digital_ok 100.00% 0.00% 0.00% 0.00% 100.00% 0.00% -0.456600 1.537032 6.627300 9.294882 0.086622 0.452347 0.505088 0.137944 0.7709 0.6916 0.4234 4.089962 2.826913
2459852 digital_ok 100.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.568704 -0.247563 6.532004 9.392688 1.233878 4.225068 4.759096 9.813927 0.8536 0.8409 0.2232 5.739842 5.024943
2459850 digital_ok 100.00% 0.00% 0.00% 0.00% 100.00% 0.00% -0.643261 0.885730 6.164129 8.817545 -0.899184 0.312415 -0.161455 0.204895 0.7753 0.7591 0.3441 3.784830 2.692302
2459849 digital_ok 100.00% 0.00% 0.00% 100.00% 100.00% 0.00% 0.700750 -0.080154 -0.473353 1.174974 6.331235 2.905898 5.651353 1.416677 0.4355 0.4300 -0.2661 4.858603 5.015745
2459848 digital_ok 100.00% 0.00% 0.00% 100.00% 100.00% 0.00% 0.441717 1.589383 5.047894 18.678089 9.606273 10.103059 3.563682 -0.575132 0.4091 0.4034 -0.2895 4.212265 4.450253
2459847 digital_ok 100.00% 0.00% 0.00% 100.00% 100.00% 0.00% 1.298429 0.593716 5.188558 17.472707 7.973532 7.716917 1.308114 -1.144715 0.3413 0.3398 -0.3150 3.960347 4.639492
2459846 digital_ok 100.00% 0.00% 0.00% 100.00% 100.00% 0.00% 2.356642 6.703070 4.199548 14.776620 0.787953 7.037446 1.435753 -0.773459 0.4227 0.4272 -0.2454 1.075785 1.214983
2459845 digital_ok 100.00% 100.00% 100.00% 0.00% 100.00% 0.00% nan nan inf inf nan nan nan nan nan nan nan 0.000000 0.000000
2459844 digital_ok 100.00% 100.00% 100.00% 0.00% - - nan nan inf inf nan nan nan nan nan nan nan nan nan
2459843 digital_ok 100.00% 100.00% 100.00% 0.00% 100.00% 0.00% nan nan inf inf nan nan nan nan nan nan nan 0.000000 0.000000
2459842 digital_ok 100.00% 0.00% 100.00% 0.00% 100.00% 0.00% 2.158823 14.001487 0.344343 8.883996 -1.402066 -0.665016 -2.118859 1.291831 0.7654 0.1129 0.5264 5.256278 1.333985
2459841 digital_ok 100.00% 100.00% 100.00% 0.00% - - nan nan inf inf nan nan nan nan nan nan nan nan nan
2459840 digital_ok 100.00% 100.00% 100.00% 0.00% - - 4.282887 3.854691 0.878401 -1.311800 5.579513 6.494461 5.434546 6.081148 0.0366 0.0429 0.0131 nan nan
2459839 digital_ok 100.00% - - - - - 0.313939 0.268526 14.679803 8.738001 4.857278 3.955389 7.227363 8.219771 nan nan nan nan nan

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

In [11]:
htmls_to_display = []
for am_html in auto_metric_htmls:
    html_to_display = ''
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line:
            htmls_to_display.append(html_to_display)
            break

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.

In [12]:
for i, html_to_display in enumerate(htmls_to_display):
    if i == 100:
        break
    display(HTML(html_to_display))

Antenna 124: 2459858

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
124 N09 digital_ok ee Temporal Variability 1.083167 0.453590 -0.589978 0.218667 -0.737321 0.460961 1.083167 0.883217 0.623461

Antenna 124: 2459857

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
124 N09 digital_ok nn Temporal Discontinuties 5.493836 1.021066 1.165751 0.184638 0.173916 1.078582 -0.915226 5.493836 0.075536

Antenna 124: 2459856

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
124 N09 digital_ok nn Shape 1.373387 -0.878193 1.373387 0.182193 -1.049791 0.071181 -1.108517 0.004725 0.620455

Antenna 124: 2459855

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
124 N09 digital_ok nn Shape 1.608484 1.608484 -1.211728 -1.315217 0.145927 -0.800515 -0.330233 -0.351288 -0.386425

Antenna 124: 2459854

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
124 N09 digital_ok nn Power 6.873543 1.791340 -1.031321 6.873543 4.728353 -0.380617 -0.165600 1.610202 1.503638

Antenna 124: 2459853

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
124 N09 digital_ok nn Power 9.294882 1.537032 -0.456600 9.294882 6.627300 0.452347 0.086622 0.137944 0.505088

Antenna 124: 2459852

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
124 N09 digital_ok nn Temporal Discontinuties 9.813927 0.568704 -0.247563 6.532004 9.392688 1.233878 4.225068 4.759096 9.813927

Antenna 124: 2459850

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
124 N09 digital_ok nn Power 8.817545 -0.643261 0.885730 6.164129 8.817545 -0.899184 0.312415 -0.161455 0.204895

Antenna 124: 2459849

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
124 N09 digital_ok ee Temporal Variability 6.331235 0.700750 -0.080154 -0.473353 1.174974 6.331235 2.905898 5.651353 1.416677

Antenna 124: 2459848

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
124 N09 digital_ok nn Power 18.678089 1.589383 0.441717 18.678089 5.047894 10.103059 9.606273 -0.575132 3.563682

Antenna 124: 2459847

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
124 N09 digital_ok nn Power 17.472707 0.593716 1.298429 17.472707 5.188558 7.716917 7.973532 -1.144715 1.308114

Antenna 124: 2459846

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
124 N09 digital_ok nn Power 14.776620 2.356642 6.703070 4.199548 14.776620 0.787953 7.037446 1.435753 -0.773459

Antenna 124: 2459845

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
124 N09 digital_ok nn Shape nan nan nan inf inf nan nan nan nan

Antenna 124: 2459844

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
124 N09 digital_ok ee Shape nan nan nan inf inf nan nan nan nan

Antenna 124: 2459843

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
124 N09 digital_ok nn Shape nan nan nan inf inf nan nan nan nan

Antenna 124: 2459842

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
124 N09 digital_ok nn Shape 14.001487 2.158823 14.001487 0.344343 8.883996 -1.402066 -0.665016 -2.118859 1.291831

Antenna 124: 2459841

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
124 N09 digital_ok ee Shape nan nan nan inf inf nan nan nan nan

Antenna 124: 2459840

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
124 N09 digital_ok nn Temporal Variability 6.494461 4.282887 3.854691 0.878401 -1.311800 5.579513 6.494461 5.434546 6.081148

Antenna 124: 2459839

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
124 N09 digital_ok ee Power 14.679803 0.268526 0.313939 8.738001 14.679803 3.955389 4.857278 8.219771 7.227363
In [ ]: